Modeling Offensive Player Movement in Professional Basketball

ABSTRACT

The 2013 arrival of SportVU player tracking data in all NBA arenas introduced an overwhelming amount of on-court information—information which the league is still learning how to maximize for insights into player performance and basketball strategy. The data contain the spatial coordinates for the ball and every player on the court for 25 frames per second, which opens up avenues of player and team performance analysis that was not possible before this technology existed. This article serves as a step-by-step guide for how to leverage a data feed from SportVU for one NBA game into visualizable components that can model any player's movement on offense. We detail some utility functions that are helpful for manipulating SportVU data before applying it to the task of visualizing player offensive movement. We conclude with visualizations of the resulting output for one NBA game, as well as what the results look like aggregated across an entire season for three NBA stars with very different offensive tendencies.

KEYWORDS:

• Data science
• Data visualization
• Sports statistics

Acknowledgment

The authors gratefully acknowledge Benjamin S. Baumer, Sean J. Taylor, and Jennifer Bryan for their gracious time in reviewing this article.

Additional information

Funding

This research was supported in part by U.S. National Science Foundation grant 1461435, by ARO under Grant No. W911NF-15-1-0172, and by NSERC.